Expectation Maximization (EM) is among the most popular algorithms for estimating parameters of statistical models. However, EM, which is an iterative algorithm based on the maximum likelihood principle, is generally only guaranteed to find stationary points of the likelihood objective, and these points may be far from any maximizer. This article addresses this disconnect between the statistica...

We study how an agent learns from endogenous data when their prior belief is misspecified. show that only uniform Berk–Nash equilibria can be long?run outcomes, and all uniformly strict have arbitrarily high probability of being the outcome for some initial beliefs. When believes distribution exogenous, every equilibrium has positive any belief. generalize these results to settings where observ...